{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append(\"/home/zhubenjin/codebase/det3d.pytorch\")\n",
    "\n",
    "import torch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline \n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.visualization.show import visualize_feature_maps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4608x4608 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 4608x4608 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task_pred = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/task_pred.bin\", dtype=np.float32).reshape(-1, 40, 2)\n",
    "visualize_feature_maps(task_pred[:, :, 0:1]) \n",
    "visualize_feature_maps(task_pred[:, :, 1:2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "task_pred_epoch_1 = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/test.bin\", dtype=np.float32).reshape(-1, 40, 2)\n",
    "visualize_feature_maps(task_pred_epoch_1[:, :, 0:1]) \n",
    "visualize_feature_maps(task_pred_epoch_1[:, :, 1:2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 12000 into shape (600,640,3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-4f406e5839be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# box = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/bev.bin\", dtype=np.uint8).reshape(600, 640, 3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbox_canonical\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/home/zhubenjin/codebase/det3d.pytorch/bev_canonical_sidev.bin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m640\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# visualize_feature_maps(box[:, :, 1:2])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mvisualize_feature_maps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox_canonical\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 12000 into shape (600,640,3)"
     ]
    }
   ],
   "source": [
    "# box = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/bev.bin\", dtype=np.uint8).reshape(600, 640, 3)\n",
    "box_canonical = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/bev_canonical_sidev.bin\", dtype=np.uint8).reshape(600, 640, 3)\n",
    "# visualize_feature_maps(box[:, :, 1:2]) \n",
    "visualize_feature_maps(box_canonical[:, :, 1:2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "task_pred_epoch_1 = np.fromfile(\"/home/zhubenjin/codebase/det3d.pytorch/task_pred_epoch_1_no_backward.bin\", dtype=np.float32).reshape(-1, 40, 2)\n",
    "visualize_feature_maps(task_pred_epoch_1[:, :, 0:1]) \n",
    "visualize_feature_maps(task_pred_epoch_1[:, :, 1:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.fromfile(\"/home/zhubenjin/repos/det3d.velocity.pytorch/labels.bin\", dtype=np.int32).reshape(500, 500, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bev = np.fromfile(\"/home/zhubenjin/repos/det3d.velocity.pytorch/bev.bin\", dtype=np.float32).reshape(1000, 1000, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_boxes = np.fromfile(\"/home/zhubenjin/repos/det3d.velocity.pytorch/gt_boxes.bin\", dtype='float64').reshape(-1, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_anchor0 = cv2.resize(labels[:, :, 0:1], (1000, 1000), interpolation=cv2.INTER_NEAREST)\n",
    "labels_anchor1 = cv2.resize(labels[:, :, 1:2], (1000, 1000), interpolation=cv2.INTER_NEAREST)\n",
    "if labels.shape[2] == 4:\n",
    "    labels_anchor2 = cv2.resize(labels[:, :, 2:3], (1000, 1000), interpolation=cv2.INTER_NEAREST)\n",
    "    labels_anchor3 = cv2.resize(labels[:, :, 3:4], (1000, 1000), interpolation=cv2.INTER_NEAREST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_boxes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.core import box_np_ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gt_boxes[:,6] = -1 * gt_boxes[:,6] - 0.5 * np.pi\n",
    "gts = box_np_ops.rbbox3d_to_corners(gt_boxes[:, [0,1,2,3,4,5,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gts = (gts//0.1 + bev.shape[0] * 0.5).astype(np.int32).transpose(0,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_feature_maps(bev, anno=gts, axes=[0,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_feature_maps(labels_anchor0[:, :, np.newaxis]+bev, anno=gts, axes=[0,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_feature_maps(labels_anchor1[:, :, np.newaxis]+bev, anno=gts, axes=[0,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_feature_maps(labels_anchor2[:, :, np.newaxis]+bev, anno=gts, axes=[0,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_feature_maps(labels_anchor3[:, :, np.newaxis]+bev, anno=gts, axes=[0,1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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